Abstract
Induction machines have extensive demand in industries as they are used for large-scale production and, therefore, vulnerable to both electrical and mechanical faults. Automated continuous condition monitoring of industrial machines to identify these faults has become one of the key areas in research for the past decade. Among various faults, early-stage identification of insulation failure in stator winding is of significant demand as it is often occurring and accounts for 37% of the overall machine failures. Also, this fault, if identified at its incipient stage, can predominantly improvise machine downtime and maintenance cost. In the proposed work, stator current signal data in the time domain from the experimental setup of both healthy and faulty induction machines are used to train the artificial neural-network models in order to identify the machine’s condition. Reducing the time required to train the neural network, features are extracted from the raw current signal data and then fed to the classifiers. Various performance characteristics of eleven neural-network models such as the number of features, number of epoch runs, training time, activation functions, learning rate, model loss function, and accuracy concerning each model are quantified. Only a few neural networks could classify a healthy and a faulty induction machine with 94.73% efficiency on generalization the neural-network model with the raw data, whereas 98.43% efficiency with the statistical featured data.
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Acknowledgements
The authors would like to express special thanks of gratitude to BITS Pilani-Hyderabad campus for the Research Initiation Grant (RIG) support.
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Appendices
Appendix 1: machine parameters
The rated parameters of the three-phase squirrel cage induction motor.
Parameter | Symbols | Ratings |
---|---|---|
Rated voltage | Vs | 400 V |
Rated current | Is | 7.3 A |
Rated power | P | 3.7 kW |
Turns number per phase | N | 432 |
Rated torque | T | 22.7 N-m |
Rated speed | N | 1440 r/min |
Frequency of power supply | F | 50 Hz |
Power factor | pf | 0.82 |
Efficiency | \(\eta\) | 86.3% |
Appendix 2: flowchart
Stop learning process for ANN when success in generalization decreases.
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Verma, A.K., Nagpal, S., Desai, A. et al. An efficient neural-network model for real-time fault detection in industrial machine. Neural Comput & Applic 33, 1297–1310 (2021). https://doi.org/10.1007/s00521-020-05033-z
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DOI: https://doi.org/10.1007/s00521-020-05033-z